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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import df2img
from myst_nb import glue
sys.path.append("../../../../indicators_setup")
from ind_setup.colors import get_df_col, plotting_style
from ind_setup.tables import plot_df_table
from ind_setup.plotting_int import plot_oni_index_th
from ind_setup.plotting import plot_bar_probs_ONI, add_oni_cat
plotting_style()
from ind_setup.core import fontsize
sys.path.append("../../../functions")
from data_downloaders import GHCN, download_oni_index
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 16
13 from myst_nb import glue
15 sys.path.append("../../../../indicators_setup")
---> 16 from ind_setup.colors import get_df_col, plotting_style
17 from ind_setup.tables import plot_df_table
18 from ind_setup.plotting_int import plot_oni_index_th
ModuleNotFoundError: No module named 'ind_setup'
country = 'Palau'
update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
Show code cell source
if update_data:
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
Show code cell source
if update_data:
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
id = 'PSW00040309' # Koror Station
dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
st_data['TMIN'] = np.where(st_data['TMIN'] >50, np.nan, st_data['TMIN'])
st_data['diff'] = st_data['TMAX'] - st_data['TMIN']
st_data['TMEAN'] = (st_data['TMAX'] + st_data['TMIN'])/2
st_data.to_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
else:
st_data = pd.read_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
df1 = download_oni_index(p_data)
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
st_data_monthly = st_data.resample('M').mean()
st_data_monthly.index = pd.DatetimeIndex(st_data_monthly.index).to_period('M').to_timestamp() + pd.offsets.MonthBegin(1)
df1['tmin'] = st_data_monthly['TMIN']
df1['tmax'] = st_data_monthly['TMAX']
df1['tdiff'] = df1['tmax'] - df1['tmin']
df1['tmean'] = (df1['tmax'] + df1['tmin'])/2
df1['tmean_ref'] = df1['tmean'] - df1.loc['1961':'1990'].tmean.mean()
df1['tmean_ref_min'] = df1['tmean'] - df1.groupby(df1.index.year).max().tmean.min()
df1 = add_oni_cat(df1, lims = lims)
df2 = df1.resample('Y').mean()
fig = plot_bar_probs_ONI(df2, var='tmean_ref')
fig.suptitle('Temperature Anomaly over the 1961-1990 mean', fontsize = fontsize)
plt.savefig(op.join(path_figs, 'F2_ST_Mean.png'), dpi=300, bbox_inches='tight')
glue("fig_ninho", fig, display=False)
plt.show()
df_format = np.round(df1.describe(), 2)
fig = plot_df_table(df_format)
df2img.save_dataframe(fig=fig, filename="getting_started.png")